- Awesome Public Datasets on GitHub - Apr 6, 2015.
A long, categorized list of large datasets (available for public use) to try your analytics skills on. Which one would you pick?
Pages: 1 2
Datasets, Finance, GitHub, Government, Machine Learning, NLP, Open Data, Time series data
- More Free Data Mining, Data Science Books and Resources - Mar 25, 2015.
More free resources and online books by leading authors about data mining, data science, machine learning, predictive analytics and statistics.
Book, Data Mining, Data Science, Free ebook, Machine Learning
- Interview: Vince Darley, King.com on the Serious Analytics behind Casual Gaming - Mar 18, 2015.
We discuss key characteristics of social gaming data, ML use cases at King, infrastructure challenges, major problems with A-B testing and recommendations to resolve them.
A/B Testing, Analytics, Gaming, Infrastructure, King.com, Machine Learning, Predictive Analysis, Vince Darley
- Machine Learning Table of Elements Decoded - Mar 11, 2015.
Machine learning packages for Python, Java, Big Data, Lua/JS/Clojure, Scala, C/C++, CV/NLP, and R/Julia are represented using a cute but ill-fitting metaphor of a periodic table. We extract the useful links.
Big Data Software, Java, Julia, Machine Learning, NLP, Python, R, Scala, scikit-learn, Weka
- 7 common mistakes when doing Machine Learning - Mar 7, 2015.
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
Pages: 1 2
Machine Learning, Mistakes, Overfitting, Regression, SVM
- Automatic Statistician and the Profoundly Desired Automation for Data Science - Feb 17, 2015.
The Automatic Statistician project by Univ. of Cambridge and MIT is pushing ahead the frontiers of automation for the selection and evaluation of machine learning models. In general, what does automation mean to Data Science?
Automation, Cambridge, Data Cleaning, Data Science, Machine Learning, MIT, Modeling, Statistician
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
convnet, Deep Learning, Ian Goodfellow, Machine Learning, Neural Networks, Yoshua Bengio, Zachary Lipton
- The High Cost of Maintaining Machine Learning Systems - Jan 21, 2015.
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
Google, Machine Learning, Software Engineering, Technical Debt, Zachary Lipton
- Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started - Jan 20, 2015.
We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.
Apache Spark, Arno Candel, Deep Learning, H2O, Machine Learning
- Why Azure ML is the Next Big Thing for Machine Learning? - Nov 17, 2014.
With advanced capabilities, free access, strong support for R, cloud hosting benefits, drag-and-drop development and many more features, Azure ML is ready to take the consumerization of ML to the next level.
Azure ML, Cloud Computing, Hadoop, Machine Learning, Marketplace, Microsoft Azure, Nate Silver, Predictive Analytics, Strata
- R and Hadoop make Machine Learning Possible for Everyone - Nov 16, 2014.
R and Hadoop make machine learning approachable enough for inexperienced users to begin analyzing and visualizing interesting data to start down the path in this lucrative field.
Data Science Skills, Hadoop, Hadoop 2.0, Joel Horwitz, LinkedIn, Machine Learning, R
- Will Deep Learning take over Machine Learning, make other algorithms obsolete? - Oct 27, 2014.
Will deep learning will take over machine learning and make other algorithms obsolete, or is it too complex to use on simpler problems? We look at both sides of this discussion.
Deep Learning, Machine Learning, Quora
- Most Viewed Machine Learning Talks at Videolectures - Sep 11, 2014.
Discover lectures from a variety of summer schools and conference tutorials on machine learning in this list of the top lectures on the subject from videolectures.net.
Machine Learning, Summer School, Tutorials, Videolectures
- Deep Learning – important resources for learning and understanding - Aug 21, 2014.
New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.
Deep Learning, Image Recognition, Machine Learning, Yann LeCun, Yoshua Bengio
- Sibyl: Google’s system for Large Scale Machine Learning - Aug 20, 2014.
A review of 2014 keynote talk about Sibyl, Google system for large scale machine learning. Parallel Boosting algorithm and several design principles are introduced.
Algorithms, Boosting, Google, Machine Learning, Sibyl
- Interview: Pedro Domingos: the Master Algorithm, new type of Deep Learning, great advice for young researchers - Aug 19, 2014.
Top researcher Pedro Domingos on useful maxims for Data Mining, Machine Learning as the Master Algorithm, new type of Deep Learning called sum-product networks, Big Data and startups, and great advice to young researchers.
Advice, Deep Learning, KDD-2014, Machine Learning, Pedro Domingos, Startups
- OpenML: Share, Discover and Do Machine Learning - Aug 11, 2014.
OpenML is designed to share, organize and reuse data, code and experiments, so that scientists can make discoveries more efficiently. It is an interesting idea to build a network of machine learning.
Kaggle, Machine Learning, OpenML, Ran Bi, Weka
- When Watson Meets Machine Learning - Jul 2, 2014.
Our report on a recent Cognitive Systems meetup co-sponsored by IBM Watson and NYU Center for Data Science, IBM Watson Ecosystem, and machine learning applications, from healthcare to cognitive toys. You will want Fang!
App, Cognitive Computing, IBM, Machine Learning, Ran Bi, Watson
- DLib: Library for Machine Learning - Jun 10, 2014.
DLib is an open source C++ library implementing a variety of machine learning algorithms, including classification, regression, clustering, data transformation, and structured prediction.
C++, DLib, Machine Learning, Open Source, Tools
- Vowpal Wabbit: Fast Learning on Big Data - May 26, 2014.
Vowpal Wabbit is a fast out-of-core machine learning system, which can learn from huge, terascale datasets faster than any other current algorithm. We also explain the cute name.
Fast Learning, John Langford, Machine Learning, Microsoft, Vowpal Wabbit
- Where to Learn Deep Learning – Courses, Tutorials, Software - May 26, 2014.
Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning.
Andrew Ng, Deep Learning, Geoff Hinton, Machine Learning, Yann LeCun
- Stacking the Deck: The Next Wave of Opportunity in Big Data - May 20, 2014.
A leading venture capitalist explains why Big Data infrastructure market is mostly mature and where lies the next big area of opportunities related to Big Data.
Chip Hazard, Full Stack Analytics, Machine Learning, Network Effects, Startups, VC
- Exclusive: Tamr at the New Frontier of Big Data Curation - May 19, 2014.
Our exclusive profile of Tamr (former Data Tamer), the latest startup from legendary Michael Stonebraker, which emerged from stealth mode to address the new field of Big Data Curation.
Andy Palmer, Data Curation, Machine Learning, Michael Brodie, Michael Stonebraker, Startups, Tamr
- Machine Learning in 7 Pictures - Mar 18, 2014.
Basic machine learning concepts of Bias vs Variance Tradeoff, Avoiding overfitting, Bayesian inference and Occam razor, Feature combination, Non-linear basis functions, and more - explained via pictures.
Basis functions, Bayesian, Concepts, Machine Learning, Pictures, Variance
